Salman et al., 2015 - Google Patents
Early detection of colorectal cancer relapse by infrared spectroscopy in “normal” anastomosis tissueSalman et al., 2015
View PDF- Document ID
- 12313267706973110325
- Author
- Salman A
- Sebbag G
- Argov S
- Mordechai S
- Sahu R
- Publication year
- Publication venue
- Journal of biomedical optics
External Links
Snippet
Colorectal cancer is one of the most aggressive cancers usually occurring in people above the age of 50 years. In the United States, colorectal cancer is the third most diagnosed cancer. The American Cancer Society has estimated 96,830 new cases of colon cancer and …
- 210000001519 tissues 0 title abstract description 32
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay
- G01N33/574—Immunoassay; Biospecific binding assay for cancer
- G01N33/57407—Specifically defined cancers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/65—Raman scattering
- G01N2021/653—Coherent methods [CARS]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Su et al. | Fourier transform infrared spectroscopy as a cancer screening and diagnostic tool: A review and prospects | |
Argov et al. | Diagnostic potential of Fourier-transform infrared microspectroscopy and advanced computational methods in colon cancer patients | |
Ghimire et al. | ATR-FTIR spectral discrimination between normal and tumorous mouse models of lymphoma and melanoma from serum samples | |
Salman et al. | Early detection of colorectal cancer relapse by infrared spectroscopy in “normal” anastomosis tissue | |
Kelly et al. | Biospectroscopy to metabolically profile biomolecular structure: a multistage approach linking computational analysis with biomarkers | |
Zhao et al. | Automated autofluorescence background subtraction algorithm for biomedical Raman spectroscopy | |
Teh et al. | Diagnosis of gastric cancer using near-infrared Raman spectroscopy and classification and regression tree techniques | |
Nallala et al. | Infrared imaging as a cancer diagnostic tool: introducing a new concept of spectral barcodes for identifying molecular changes in colon tumors | |
Majeed et al. | Quantitative histopathology of stained tissues using color spatial light interference microscopy (cSLIM) | |
Nallala et al. | Infrared spectral imaging as a novel approach for histopathological recognition in colon cancer diagnosis | |
Adur et al. | Nonlinear optical microscopy signal processing strategies in cancer | |
Tomas et al. | Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks | |
Kwak et al. | Automated prostate tissue referencing for cancer detection and diagnosis | |
Wolthuis et al. | IR spectral imaging for histopathological characterization of xenografted human colon carcinomas | |
Chaber et al. | Distinguishing Ewing sarcoma and osteomyelitis using FTIR spectroscopy | |
Tiwari et al. | Extracting knowledge from chemical imaging data using computational algorithms for digital cancer diagnosis | |
Walsh et al. | FTIR microspectroscopy coupled with two-class discrimination segregates markers responsible for inter-and intra-category variance in exfoliative cervical cytology | |
Zheng et al. | Sensitivity map of laser tweezers Raman spectroscopy for single-cell analysis of colorectal cancer | |
Mayerich et al. | Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data | |
Magalhães et al. | Raman spectroscopy with a 1064-nm wavelength laser as a potential molecular tool for prostate cancer diagnosis: a pilot study | |
Khanmohammadi et al. | Cancer diagnosis by discrimination between normal and malignant human blood samples using attenuated total reflectance-fourier transform infrared spectroscopy | |
Tiwari et al. | Colon cancer grading using infrared spectroscopic imaging-based deep learning | |
Rutter et al. | Identification of a glass substrate to study cells using fourier transform infrared spectroscopy: are we closer to spectral pathology? | |
Li et al. | Detection of gastric cancer with Fourier transform infrared spectroscopy and support vector machine classification | |
Siqueira et al. | SVM for FT‐MIR prostate cancer classification: An alternative to the traditional methods |